DocumentCode :
327526
Title :
Knowledge discovery from low quality meteorological databases
Author :
Rayward-Smith, V.J.
fYear :
1998
fDate :
35923
Firstpage :
42461
Lastpage :
42465
Abstract :
The authors consider a meteorological application for KDD. The formatting of meteorological problems can yield extremely wide databases, abundant with missing values and unreliable data. They show how feature selection can be applied to remove irrelevant fields from the database thus creating a problem of workable proportions for later stages. Simulated annealing is used to extract rules describing the various outcomes and finally the results are analysed in the context of the problem domain
Keywords :
meteorology; feature selection; irrelevant field removal; knowledge discovery; low quality meteorological databases; outcomes; rule extraction; simulated annealing;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Knowledge Discovery and Data Mining (1998/434), IEE Colloquium on
Conference_Location :
London
Type :
conf
DOI :
10.1049/ic:19980644
Filename :
710058
Link To Document :
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